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Weighting Finite-State Transductions With Neural Context
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Eisner, Jason; Cotterell, Ryan; Rastogi, Pushpendre. - : Association for Computational Linguistics, 2016. : Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016
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Abstract:
How should one apply deep learning to tasks such as morphological reinflection, which stochastically edit one string to get another? A recent approach to such sequence-to-sequence tasks is to compress the input string into a vector that is then used to generate the out- put string, using recurrent neural networks. In contrast, we propose to keep the traditional architecture, which uses a finite-state trans- ducer to score all possible output strings, but to augment the scoring function with the help of recurrent networks. A stack of bidirec- tional LSTMs reads the input string from left- to-right and right-to-left, in order to summa- rize the input context in which a transducer arc is applied. We combine these learned fea- tures with the transducer to define a probabil- ity distribution over aligned output strings, in the form of a weighted finite-state automaton. This reduces hand-engineering of features, al- lows learned features to examine unbounded context in the input string, and still permits ex- act inference through dynamic programming. We illustrate our method on the tasks of mor- phological reinflection and lemmatization.
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URL: https://www.repository.cam.ac.uk/handle/1810/294482 https://doi.org/10.17863/CAM.41588
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A Joint Model of Orthography and Morphological Segmentation
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Cotterell, Ryan; Vieira, Tim; Schütze, Hinrich. - : Association for Computational Linguistics, 2016. : Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016
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